Research Scientist Intern - Neural Representations Compression

In the rapidly evolving fields of AI, Augmented Reality (AR), and Virtual Reality (VR), industry compression research is shifting from human-consumed media to non-human-consumed internal neural model states. These representations include latent tensors, weights, Gaussian splats, feature maps, and depth/alpha buffers exchanged between neural networks. As high-capability models exceed on-device compute limits while cloud execution is constrained by latency, privacy, and power, systems are adopting split inference—partitioning model execution between devices and datacenters connected by neural bitstreams.

In this architecture, the compression ratio on the split link serves as a primary determinant of round-trip latency, bandwidth efficiency, and battery performance. Advancing compression techniques for neural representations expands beyond rendering outputs to critical non-media data streams. Key emergent areas include Feature Coding for Machines (FCM) for intermediate network features, visual and multimodal token pruning/compression, low-latency action and control streams for embodied systems, KV cache compression for distributed LLM prefill and decode stages, model deltas for on-device personalization, and persistent scene state streams for interactive world models.